Deep Learning (DL) frameworks are fundamental components of DL systems in their development, deployment, and execution, while defects in DL frameworks can cause severe consequences. Ensuring the quality of DL frameworks has therefore become a pressing challenge. Among the various testing techniques, model mutation has emerged as a widely adopted approach. Such methods generate mutants by applying mutation operators to DL models (e.g., structural changes or parameter edits) and then analyzing inconsistencies, crashes, or abnormal behaviors across different frameworks or hardware. Despite its effectiveness, existing methods suffer from the following limitations. First, they mainly reuse operators designed for model testing, raising doubts about their ability to expose framework-level defects. Besides, they insufficiently consider mutation constraints, such as mutation type, position, and order, which directly affect the defect detection ability of generated mutants. Finally, they rely on the limited detection range and narrow test oracles, focusing on functional correctness in model inference while overlooking defects in efficiency, resource usage, and other defects that developers care about in other stages, such as model training or deployment. These limitations result in a weak alignment with the critical defects that developers are most concerned about in practice. Motivated by these observations, this study conducts a comprehensive investigation into the effectiveness of existing mutation-based testing methods. We first collect and classify defect reports from PyTorch and MindSpore according to developers’ priority tags, building a taxonomy of seven categories and 19 sub-categories of HP defects. We then map the defects reported by five state-of-the-art methods into this taxonomy to evaluate their detection abilities. To explain these limitations, we further analyze how three key factors, mutation type, mutation position, and mutation order, affect the generated mutants. Based on the experiment results, we summarize ten findings ranging from revealing the priority of developers on fixing framework defects, evaluating the defect detection ability of existing methods, to how mutation factors affect the generated mutants. Furthermore, we reveal four limitations and their root causes of existing methods and propose four targeted optimization strategies. We further apply these strategies to COMET and successfully uncover six new defects spanning four types, including two previously unreported categories. Overall, our study identifies 38 unique framework defects, of which 30 are confirmed by developers and 12 have been fixed, demonstrating the practical value of our findings.

Perceiving the complex driving environment precisely is crucial to the safe operation of autonomous vehicles. With the tremendous advancement of deep learning and communication technology, Vehicle-to-Everything (V2X) cooperative perception has the potential to address limitations in sensing distant objects and occlusion for a single-agent perception system. V2X cooperative perception systems are software systems characterized by diverse sensor types and cooperative agents, varying fusion schemes, and operation under different communication conditions. Therefore, their complex composition gives rise to numerous operational challenges. Furthermore, when cooperative perception systems produce erroneous predictions, the types of errors and their underlying causes remain insufficiently explored.To bridge this gap, we take an initial step by conducting an empirical study of V2X cooperative perception. To systematically evaluate the impact of cooperative perception on the ego vehicle’s perception performance, we identify and analyze six prevalent error patterns in cooperative perception systems. We further conduct a systematic evaluation of the critical components of these systems through our large-scale study and identify the following key findings: (1) The LiDAR-based cooperation configuration exhibits the highest perception performance; (2) Vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication exhibit distinct cooperative perception performance under different fusion schemes; (3) Increased cooperative perception errors may result in a higher frequency of driving violations; (4) Cooperative perception systems are not robust against communication interference when running online. Our results reveal potential risks and vulnerabilities in critical components of cooperative perception systems. We hope that our findings can better promote the design and repair of cooperative perception systems.

Deep learning (DL) frameworks are the fundamental infrastructure for various DL applications. Framework defects can profoundly cause disastrous accidents, thus requiring sufficient detection. In previous studies, researchers adopt DL models as test inputs combined with mutation to generate more diverse models. Though these studies demonstrate promising results, most detected defects are considered trivial (i.e., either treated as edge cases or ignored by the developers). To identify important bugs that matter to developers, we propose a novel DL framework testing method DevMuT, which generates models by adopting mutation operators and constraints derived from developer expertise. DevMuT simulates developers' common operations in development and detects more diverse defects within more stages of the DL model lifecycle (e.g., model training and inference). We evaluate the performance of DevMuT on three widely used DL frameworks (i.e., PyTorch, JAX, and Mind-Spore) with 29 DL models from nine types of industry tasks. The experiment results show that DevMuT outperforms state-of-the-art baselines: it can achieve at least 71.68% improvement on average in the diversity of generated models and 28.20% improvement on average in the legal rates of generated models. Moreover, DevMuT detects 117 defects, 63 of which are confirmed, 24 are fixed, and eight are of high value confirmed by developers. Finally, DevMuT has been deployed in the MindSpore community since December 2023. These demonstrate the effectiveness of DevMuT in detecting defects that are close to the real scenes and are of concern to developers.

Concurrency programs often induce buggy results due to the unexpected interaction among threads. The detection of these concurrency bugs costs a lot because they usually appear under a specific execution trace. How to virtually explore different thread schedules to detect concurrency bugs efficiently is an important research topic. Many techniques have been proposed, including lightweight techniques like adaptive randomized scheduling (ARS) and heavyweight techniques like maximal causality reduction (MCR). Compared to heavyweight techniques, ARS is efficient in exploring different schedulings and achieves state-of-the-art performance. However, it will lead to explore large numbers of redundant thread schedulings, which will reduce the efficiency. Moreover, it suffers from the “cold start” issue, when little information is available to guide the distance calculation at the beginning of the exploration. In this work, we propose a Heuristic-Enhanced Adaptive Randomized Scheduling (HARS) algorithm, which improves ARS to detect concurrency bugs guided with novel distance metrics and heuristics obtained from existing research findings. Compared with the adaptive randomized scheduling method, it can more effectively distinguish the traces that may contain concurrency bugs and avoid redundant schedules, thus exploring diverse thread schedules effectively. We conduct an evaluation on 45 concurrency Java programs. The evaluation results show that our algorithm performs more stably in terms of effectiveness and efficiency in detecting concurrency bugs. Notably, HARS detects hard-to-expose bugs more effectively, where the buggy traces are rare or the bug triggering conditions are tricky.

Abstract Cross‐project defect prediction (CPDP) is needed when the target projects are new projects or the projects have less training data, since these projects do not have sufficient historical data to build high‐quality prediction models. The researchers have proposed many CPDP methods, and previous studies have conducted extensive comparisons on the performance of different CPDP methods. However, to the best of our knowledge, it remains unclear whether different CPDP methods can identify the same defective modules, and this issue has not been thoroughly explored. In this article, we select 12 state‐of‐the‐art CPDP methods, including eight supervised methods and four unsupervised methods. We first compare the performance of these methods in the same experiment settings on five widely used datasets (ie, NASA, SOFTLAB, PROMISE, AEEEM, and ReLink) and rank these methods via the Scott‐Knott test. Final results confirm the competitiveness of unsupervised methods. Then we perform diversity analysis on defective modules for these methods by using the McNemar test. Empirical results verify that different CPDP methods may lead to difference in the modules predicted as defective, especially when the comparison is performed between the supervised methods and unsupervised methods. Finally, we also find there exist a certain number of defective modules, which cannot be correctly identified by any of the CPDP methods or can be correctly identified by only one CPDP method. These findings can be utilized to design more effective methods to further improve the performance of CPDP.

Cross-project defect prediction (CPDP) as a means to focus quality assurance of software projects was under heavy investigation in recent years. In this paper, we propose a novel CPDP approach via deep learning. In particular, we model each program module via simplified abstract syntax tree (S-AST). For each node in S-AST, only the project-independent node type is remained and other project-specific information (such as name of variable and method) is ignored, so that the modeling method is project-independent and suitable for CPDP issue. Then we extract token sequences from program modules modeled as S-AST. In addition, to construct meaningful vector representations for token sequences, we propose a novel unsupervised embedding method ASTToken2Vec, which learns semantic information from S-AST's natural structure. Finally, we use BLSTM (bi-directional long short-term memory) based neural network to automatically learn semantic features from vectorized token sequences and construct CPDP models. In our empirical studies, 10 real large-scale open source Java projects are chosen as our empirical subjects. Final results show that our proposed CPDP approach can perform significantly better than 5 state-of-the-art CPDP baselines in terms of AUC.